Human face detection method

ABSTRACT

A human face detection method, falling within the technical field of image detection. The method comprises: respectively determining a plurality of pieces of human face characteristic information in a plurality of pre-input human face training samples, and training and forming a characteristic prediction model according to all the pieces of human face characteristic information in each of the human face training samples. The method further comprises: step S 1 , using an image acquisition apparatus to acquire an image; step S 2 , using a human face detector trained and formed in advance to determine whether the image comprises a human face, and if not, returning back to step S 1 ; step S 3 , using a characteristic prediction model to obtain a plurality of pieces of human face characteristic information through prediction from the human face in the image; and step S 4 , constituting a facial structure associated with the human face according to the plurality of pieces of human face characteristic information obtained through prediction, and subsequently quitting. The beneficial effects of the technical solution are: being able to detect information about a human face comprising parts, such as the five sense organs and an outer profile, and improving the accuracy of human face detection.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to and the benefit of Ser. No.PCT/CN2017/074061 filed Feb. 20, 2017, the entire contents of which areincorporated by reference, which in turn claims priority to and thebenefit of Chinese Patent Application No. CN 201610099902.4 filed onFeb. 23, 2016, the entire content of which is incorporated herein byreference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to the technical field of image detection,especially to a method for human face detection.

2. Description of the Related Art

With the rapid development of technology of intelligent devices, variousinformation interaction approaches are developed in intelligent devices,so as to enable a user to be liberated from traditional informationinput methods, such as inputting with a keyboard and a mouse. Newinformation interaction approaches, such as voice interaction approach,gesture interaction approach, face recognition interaction approach andfingerprint identification interaction approach begin to be applied tothe intelligent devices, such as an intelligent robot.

In existing techniques of face recognition interaction approach, themost important part is how to ensure the accuracy of face recognition.In other words, in order to ensure effectiveness of the face recognitioninteraction approach, the accuracy of face recognition must be ensuredfirst.

For example, the face recognition interaction approach is utilized forAWU (Auto Wake Up) of the intelligent device, wherein if a face isrecognized as a specific user, the intelligent device will beautomatically waked up, if not, the intelligent device cannot be wakedup. Under this circumstance, if the accuracy of face recognition isinadequate, following situation may occur: 1) the specific user cannotbe recognized and the function of AWU will not be achieved, on thecontrary, complexity of operation for the user will be increased; 2)misrecognition may occur, in other words, a face not corresponding tothe specific user may be recognized as the face associated with thespecific user, so that the function of AWU will be activated, therebyincreasing potential possibility of privacy leak in the intelligentdevice.

The face recognition technology in prior art usually can only recognizea face facing towards a camera, if there is an angle between the face ofa user and the camera, recognition accuracy will decrease drastically.

SUMMARY OF THE INVENTION

Aiming at the abovementioned technical problems, the invention providesa technical solution of a method for human face detection. The technicalsolution is intended to detect information on facial features includingfive sense organs and outer facial contour in a human face, so as toincrease the accuracy of face detection.

The technical solution specifically comprises:

a method for human face detection comprises:

determining a plurality of pieces of face feature informationrespectively in a plurality of pre-input face training samples, andtraining to form a feature prediction model according to all the facefeature information in each of the face training samples, wherein themethod further comprises:

Step S1: an image capture device is used to capture images;

Step S2: a face detector formed in an advance training is used todetermine whether any human face is included in the image;

if the result shows “NO”, returning to Step S1;

Step S3: the feature prediction model is used to obtain a plurality ofpieces of face feature information through prediction from the humanface in the image; and

Step S4: constituting a facial structure associated with the human facebased on the plurality of pieces of face feature information obtainedthrough prediction, then quitting.

Preferably, the method for human face detection, wherein steps oftraining to form the feature prediction model specifically comprises:

Step A1: a plurality of face images at various angles are obtained asthe face training samples;

Step A2: on a preset face training sample, the plurality of pieces offace feature information are labeled as input information, and angleinformation associated with the preset face training sample is set asoutput information, so as to train the feature prediction model;

Step A3: determining whether there is any face training sample which isnot as a basis of training yet:

if the result shows “YES”, a final feature prediction model is formedand output, then quitting;

if the result shows “NO”, returning to Step A2 to train the featureprediction model according to next face training sample.

Preferably, the method for human face detection, wherein in Step A2, alogical regression algorithm is used, with the plurality of pieces offace feature information being set as the input information, and theangle information as the output information, so as to train the featureprediction model.

Preferably, the method for human face detection, wherein in Step A2, aneural network regression algorithm is used, with the plurality ofpieces of face feature information being set as the input information,and the angle information as the output information, so as to train thefeature prediction model.

Preferably, the method for human face detection, wherein in Step A2, amethod for obtaining the angle information associated with the facetraining sample, comprising:

obtaining corresponding angle information based on different anglesbetween the obtained face images and the image capture device inadvance.

Preferably, the method for human face detection, wherein in Step A2, amethod for obtaining the angle information associated with the facetraining sample, comprising:

obtaining two arbitrary points on facial feature profile associated withthe human face according to the face training sample, and determiningthe angle information associated with the face training sample accordingto an angle between a line joining the two arbitrary points and X-axisof the image coordinate; or

obtaining two arbitrary points on facial feature profile associated withthe human face according to the face training sample, and determiningthe angle information associated with the face training sample accordingto an angle between a line joining the two arbitrary points and Y-axisof the image coordinate.

Preferably, the method for human face detection, wherein each piece ofthe face feature information is used for representing one feature pointin the human face.

Preferably, the method for human face detection, wherein the featurepoints comprises:

a feature point for representing eyebrows in the human face; and/or

a feature point for representing eyes in the human face; and/or

a feature point for representing a nose in the human face; and/or

a feature point for representing a mouth in the human face; and/or

a feature point for representing a whole outer facial contour in thehuman face.

Preferably, the method for human face detection, wherein in Step S2, ifa human face is included in the image, location information and sizeinformation of the human face in the image will be obtained, thenproceeding to Step S3;

and in Step S3, based on the location information and the sizeinformation of the human face, the feature prediction model is used toobtain a plurality of pieces of the face feature information from thehuman face in the image through prediction.

The advantageous effects of the invention includes: a method for humanface detection is provided, and the method could detect information onfacial features including five senses organs and outer facial contour ina human face, so as to increase the accuracy of face detection.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The accompanying drawings, together with the specification, illustrateexemplary embodiments of the present disclosure, and, together with thedescription, serve to explain the principles of the present invention.

FIG. 1 is a general flow diagram of a method for human face detectionaccording to a preferred embodiment of the invention.

FIG. 2 is a flow diagram of feature prediction model formed in anadvance training according to a preferred embodiment of the invention.

DETAILED DESCRIPTION

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which exemplary embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likereference numerals refer to like elements throughout.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” or “includes” and/or “including” or “has” and/or“having” when used herein, specify the presence of stated features,regions, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

As used herein, “around”, “about” or “approximately” shall generallymean within 20 percent, preferably within 10 percent, and morepreferably within 5 percent of a given value or range. Numericalquantities given herein are approximate, meaning that the term “around”,“about” or “approximately” can be inferred if not expressly stated.

As used herein, the term “plurality” means a number greater than one.

Hereinafter, certain exemplary embodiments according to the presentdisclosure will be described with reference to the accompanyingdrawings.

In a preferred embodiment of the invention, aiming at solving theabovementioned technical problems in the prior art, a method for humanface detection is provided, and the method can be applied to intelligentdevices, especially those capable of performing information interactionwith a user, such as an intelligent robot. Specifically, applying themethod for human face detection to the intelligent robot, and theintelligent robot could execute corresponding operation by detecting theface of a user. For instance, if the face of a specific user isdetected, the intelligent robot will be waked up and ready for receivingfurther instructions from the user.

The specific steps of the method for human face detection are shown inFIG. 1, comprising:

Step S1: an image capture device is used to capture images;

Step S2: a ace detector forms in an advance training is used todetermine whether a human face is included in the image;

if the result shows “NO”, returning to Step S1;

Step S3: the feature prediction model is used to obtain a plurality ofpieces of face feature information through prediction from the humanface in the image; and

Step S4: constituting a facial structure associated with the human facebased on the plurality of pieces of face feature information obtainedthrough prediction, then quitting.

In a specific embodiment, firstly, the image capture device is used tocapture images right in front of the image capture device. The imagecapture device could be a camera, such as a camera configured on thehead of the intelligent robot.

In this embodiment, after images being captured by the image capturedevice, a face detector formed in an advance training is used todetermine whether any human face is included in the image: if the resultshows “YES”, proceeding to Step S3; if the result shows “NO”, returningto Step S1. Specifically, the face detector is a detection model, whichcould be formed in an advance training and be used to recognize a humanface. In prior art, there are many technical solutions for training thedetection model, thus, it is not necessary to give details herein.

In this embodiment, after a human face in the image being detected, thefeature prediction model formed in an advance training is used to obtainthe plurality of pieces of face feature information through predictionfrom the human face in the image. Specifically, the feature predictionmodel is formed by steps as follows: determining the plurality of piecesof face feature information respectively within pre-input multiple facetraining samples, and training to form the feature prediction modelaccording to all the face feature information in each of the facetraining samples. Specific steps of forming the feature prediction modelwould be described in detail hereinafter.

In this embodiment, the face feature information could be used torepresent feature information in various parts of the face, for example,including at least one of the followings:

a feature point for representing eyebrows in the human face; and/or

a feature point for representing eyes in the human face; and/or

a feature point for representing a nose in the human face; and/or

a feature point for representing a mouth in the human face; and/or

a feature point for representing a whole outer facial contour in thehuman face.

In other words, the face feature information is used for describinginformation of profile of various parts in the face. Thus, in theembodiment, after the face feature information is obtained throughprediction by the feature prediction model formed in an advancetraining, facial characteristics of the face is obtained by the obtainedface feature information, so that the whole facial structure of the faceis obtained, thus, accuracy of face detection can be increasedsignificantly.

In a preferred embodiment, in Step S2, if it is determined that a humanface is included in the image, location information and size informationof the human face in the image will be obtained, then proceeding to StepS3;

specifically, the location information refers to the location of a facein the whole image; the size information refers to the size of the facein the whole image, which could be measured by pixels.

Then in Step S3, based on the location information and the sizeinformation of the human face, the feature prediction model is used toobtain the plurality of pieces of the face feature information throughprediction from the human face in the image.

In a preferred embodiment of the present invention, as shown in FIG. 2,steps of training to form the feature prediction model specificallycomprise:

Step A1: a plurality of face images at various angles are obtained asthe face training samples;

Step A2: on a pre-set face training sample, the plurality of pieces offace feature information are labeled as input information, and angleinformation associated with the pre-set face training sample is set asoutput information, to train the feature prediction model;

Step A3: estimating whether there is any face training sample which isnot as a basis of training yet:

if the result shows “YES”, the final feature prediction model is formedand output, then quitting;

if the result shows “NO”, returning to Step A2 to train the featureprediction model according to a next face training sample.

In a preferred embodiment of the present invention, the plurality offace training samples could be 100 face training samples, in otherwords, arranging for 100 average persons to stand in front of the imagecapture device for inputting corresponding faces as the face trainingsamples.

Specifically, the face detection methods in prior art usually require auser to stand in the place just in front of the camera and the face mustbe facing towards the camera, in order to ensure the accuracy of facedetection. If there is a specific angle between the face of the user andthe camera, accuracy of face detection will be directly affected.Moreover, in a preferred embodiment, for solving this problem, theabovementioned feature prediction model is formed in an advancetraining, in other words:

firstly, a plurality of face images at various angles are obtained asthe face training samples; with respect to each of the face trainingsamples, an angle information of the face training sample relative tothe camera should be obtained. The method for obtaining the angleinformation may comprise:

(1) the angle information is pre-obtained based on the angle between theface and the camera; for example, a user is arranged to stand in aposition in a known angle relative to the camera in advance, so that thecamera would capture a face image, then the known angle would berecorded as the angle information corresponding to the face trainingsample; or

(2) obtaining two arbitrary points on the facial feature profileassociated with the human face according to the face training sample,and determining the angle information associated with the face trainingsample according to an angle between a line joining the two arbitrarypoints and X-axis of the image coordinate; or

(3) obtaining two arbitrary points on the facial feature profileassociated with the human face according to the face training sample,and determining the angle information associated with the face trainingsample according to an angle between a line joining the two arbitrarypoints and Y-axis of the image coordinate.

In other words, the method for obtaining the angle information maycomprise getting the pre-set angle information directly, or getting theangle information according to an angle between a line joining twoarbitrary points on facial feature profile associated with the humanface and X/Y-axis of the image coordinate.

In a preferred embodiment of the present invention, in Step A2, aplurality of pieces of face feature information are labeled as inputinformation on a pre-set face training sample. Specifically, 68 featurepoints may be labeled on a pre-set face training sample as the facefeature information according to preset criteria. As described above,the 68 pieces of the face feature information could be used to representcharacteristic of a mouth, eyes, eyebrows, a nose, a facial contour andso on in the human face respectively, that is to say, the 68 pieces ofthe face feature information may be used to substantially describe thewhole facial profile, including profile of various parts in the face. Inother embodiments of the present invention, according to practicalrequirements for recognition accuracy, labels of the face featureinformation could be increased or decreased.

In a preferred embodiment of the present invention, with respect to apre-set face training sample, a plurality of pieces of face featureinformation (e.g. 68 pieces of the face feature information) associatedwith the face training sample are set as input information, and angleinformation between the face in the face training sample and the camerais set as output information, so that the feature prediction model istrained.

Specifically, in a preferred embodiment of the present invention, theprocess of training to form the feature prediction model could beachieved by a logical regression algorithm, wherein the face featureinformation are set as the input information and input into a logicalregression model trainer, and the corresponding angle information is setas the output information, so that the corresponding feature predictionmodel is obtained by using the logical regression algorithm.

In another preferred embodiment of the present invention, the process oftraining to form the feature prediction model could also be achieved bya neural network regression algorithm, wherein the face featureinformation are set as the input information, and the angle informationis set as the output information, so that the feature prediction modelis obtained through training.

In other embodiments of the present invention, the process of trainingto form the feature prediction model could further be achieved by otherparameter regression algorithms, and it is not given unnecessary detailsherein.

In a preferred embodiment of the present invention, a final featureprediction model formed through training may be a SVM (Support VectorMachine) model. In practical applications, in Step S3, firstly, thefeature prediction model is used to predict the plurality of pieces offace feature information through prediction from the human face in theimage, such as 68 pieces of the face feature information obtainedthrough prediction. Subsequently, a feature location information (afigure showing mutual location relationship) constituted by 68 pieces ofthe face feature information is input into the feature prediction model(SVM model), so as to get an angle information of the face, therebyobtaining relevant information on a facial structure associated with thehuman face at last.

In conclusion, the technical solution of the present invention providesa method for human face detection, wherein a feature prediction model,which is formed in an advance training, is utilized to obtain aplurality of pieces of face feature information through prediction in ahuman face according to various angles between the human face and animage capture device, so as to form a general facial structure of thehuman face, thereby increasing the accuracy of face detection.

These embodiments shown above represent only preferred examples of thepresent invention and may therefore not be understood to be limiting ofthe embodiments and scope of the invention. Alternative embodiments thatcan be contemplated by the person skilled in the art are likewiseincluded in the scope of the present invention.

What is claimed is:
 1. A method for human face detection, comprising:determining a plurality of pieces of face feature informationrespectively in a plurality of pre-input face training samples, andtraining to form a feature prediction model according to all the facefeature information in each of the face training samples, wherein themethod further comprises: Step S1: an image capture device is used tocapture images; Step S2: a face detector formed in an advance trainingis used to determine whether a human face is included in the image; ifnot, returning to Step S1; Step S3: the feature prediction model is usedto obtain a plurality of pieces of face feature information throughprediction from the human face in the image; and Step S4: constituting afacial structure associated with the human face based on the pluralityof pieces of face feature information obtained through prediction, thenquitting; wherein steps of training to form the feature prediction modelspecifically comprises: Step A1: a plurality of face images at variousangles are obtained as the face training samples; Step A2: on a presetface training sample, the plurality of pieces of face featureinformation are labeled as input information, and angle informationassociated with the preset face training sample is set as outputinformation, to train the feature prediction model; Step A3: determiningwhether there is any face training sample which is not as a basis oftraining yet: if the result shows “YES”, the final feature predictionmodel is formed and output, then quitting; if the result shows “NO”,returning to Step A2 to train the feature prediction model according toa next face training sample.
 2. The method for human face detection asclaimed in claim 1, wherein in Step A2, a logical regression algorithmis used, with the plurality of pieces of face feature information beingset as the input information, and the angle information as the outputinformation, so as to train the feature prediction model.
 3. The methodfor human face detection as claimed in claim 1, wherein in Step A2, aneural network regression algorithm is used, with the plurality ofpieces of face feature information being set as the input information,and the angle information as the output information, so as to train thefeature prediction model.
 4. The method for human face detection asclaimed in claim 1, wherein in Step A2, a method for obtaining the angleinformation associated with the face training sample, comprising:obtaining corresponding angle information based on different anglesbetween the obtained face image and the image capture device in advance.5. The method for human face detection as claimed in claim 1, wherein inStep A2, a method for obtaining the angle information associated withthe face training sample, comprising: obtaining two arbitrary points onfacial feature profile associated with the human face according to theface training sample, and determining the angle information associatedwith the face training sample according to an angle between a linejoining the two arbitrary points and X-axis of the image coordinate; orobtaining two arbitrary points on facial feature profile associated withthe human face according to the face training sample, and determiningthe angle information associated with the face training sample accordingto an angle between a line joining the two arbitrary points and Y-axisof the image coordinate.
 6. The method for human face detection asclaimed in claim 1, wherein each piece of the face feature informationis used for representing one feature point in the human face.
 7. Themethod for human face detection as claimed in claim 6, wherein thefeature points comprise: a feature point for representing eyebrows inthe human face; and/or a feature point for representing eyes in thehuman face; and/or a feature point for representing a nose in the humanface; and/or a feature point for representing a mouth in the human face;and/or a feature point for representing a whole outer facial contour inthe human face.
 8. The method for human face detection as claimed inclaim 1, wherein in Step S2, if it is determined that a human face isincluded in the image, location information and size information of thehuman face in the image will be obtained, then proceeding to Step S3;and in Step S3, based on the location information and the sizeinformation of the human face, the feature prediction model is used toobtain a plurality of pieces of the face feature information throughprediction from the human face in the image.